Remember domino theory? One country going Communist was supposed to topple the next, and then the next, and the next. The metaphor drove much of United States foreign policy in the middle of the 20th century. But it had the wrong name. From a physical point of view, it should have been called the “sandpile theory.”Real-world political phase transitions tend to happen not in neat sequences, but in sudden coordinated fits, like the Arab Spring, or the collapse of the Eastern Bloc. These reflect quiet periods punctuated by crises—like a sandpile. You can add grains of sand to the top of a sandpile for a while, to no apparent effect. Then, all at once, an avalanche sweeps sand down from the top in an irregular pattern, possibly setting off little sub-avalanches as it goes.

The Intergovernmental Panel on Climate Change (IPCC) is becoming irrelevant to climate policy. By seeking consensus and avoiding controversy, the organization is suffering from the streetlight effect — focusing ever more attention on a well-lit pool of the brightest climate science. But the insights that matter are out in the darkness, far from the places that the natural sciences alone can illuminate.

Climate change: Embed the social sciences in climate policy David Victor

Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraint-based interpretation of emergence and arguing that such accounts recover many of the features of more traditional accounts. We show that the constraint-based account of emergence also leads naturally into a meaningful definition of self-organization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper.

We are naturally constrained by many natural laws in our universe. Our governments are likewise constrained by physical laws of nature as well as the natural laws behind people, societies, economies, and ecosystems. Where the constraints came from in nature, I don't know. But what I do see, is that like the natural laws of the universe, societies impose other constraints upon our actions, behaviors, perceptions, chosen courses of action, abilities to frame issues and topics, abilities to define conditions within our social systems. Governments can likewise make and define constraints for behaviors or willingness and ability to behave on the part of the citizenry, either by offering incentives to get people to behave in a particular way or to penalize and possibly limit some actions and chosen patterns of behavior.

It should be noted that the laws and chosen constraints and incentives of the government on this level of existence can only be as good as the people who sit within them and make choices. They are also limited by the physical laws of the universe and the natural laws, conditions, desires, and motives of the general public that composes the whole of society in aggregate and as that which is greater than the aggregate; the combined whole of human thought, behavior, and sentiment.

These human-made constraints (created by governments and social authority figures) are also imperfect in their ability to contain and constrain the society, since the society and its members have autonomy from the government. Humans and human societies are more constrained by the natural laws and the limitations of knowledge and perception that are present in our brains and neural systems. Therefore, it can be said that human-made social constraints are less important than the natural ones that exist amongst ourselves and within the universe that we are apart of.

Therefore, I think that in order to continue to advance humanity and contribute to our potential to survive, endure, and thrive, we should be constantly and safely pushing at the constraints of what we already know and can do as individuals and as a species. Our government(s) should focus on studying the universal natural laws of societies, economies, human behavior, and environmental functions in addition to the particular laws of their own societies, making laws and legal systems that work better and better with the natural laws of their own societies and amongst all human societies. We should capitalize on our differences of perspective and opinion, sifting out those that don't fall into line with discovered reality while using that which is accurate to complete the puzzles of our universe in order to produce something greater than what we've presently got and to continue to advance ourselves safely and in accordance with what is actually helpful, healthful, and ethical for all sentient life in the universe. Study, research, observation, and exploration are what will make tomorrow better than today, even as the natural laws and some conditions remain the same. Health, well-being, quality of life, sustainability, and the ability to thrive for all are what we need to prioritize and produce as a society over financial profits and short term economic gains for a few. Some constraints can be pushed, some can't, and some really shouldn't from the perspective of health, well-being, quality of life, and the ability to thrive for all. Welcome to nature.

Making toast doesn’t sound very complicated -- until someone asks you to draw the process, step by step. Tom Wujec loves asking people and teams to draw how they make toast, because the process reveals unexpected truths about how we can solve our biggest, most complicated problems at work. Learn how to run this exercise yourself, and hear Wujec’s surprising insights from watching thousands of people draw toast.

To maintain stability yet retain the flexibility to adapt to changing circumstances, social systems must strike a balance between the maintenance of a shared reality and the survival of minority opinion. A computational model is presented that investigates the interplay of two basic, oppositional social processes—conformity and anticonformity—in promoting the emergence of this balance. Computer simulations employing a cellular automata platform tested hypotheses concerning the survival of minority opinion and the maintenance of system stability for different proportions of anticonformity. Results revealed that a relatively small proportion of anticonformists facilitated the survival of a minority opinion held by a larger number of conformists who would otherwise succumb to pressures for social consensus. Beyond a critical threshold, however, increased proportions of anticonformists undermined social stability. Understanding the adaptive benefits of balanced oppositional forces has implications for optimal functioning in psychological and social processes in general.

Computers aren't best suited to visual object recognition. Our brains are hardwired to quickly see and match patterns in everything, with great leaps of intuition, while the processing center of a computer is more akin to a very powerful calculator. But that hasn't stopped neuroscientists and computer scientists from trying over the past 40 years to design computer networks that mimic our visual skills. Recent advances in computing power and deep learning algorithms have accelerated that process to the point where a group of MIT neuroscientists has found a network design that compares favorably to the brain of our primate cousins.

This is important beyond the needs of automated digital information processing like Google's image search. Computer-based neural networks that work like the human brain will further our understanding of how the brain works, and any attempts to create them will test that understanding. Essentially, the fact that these networks work to a level comparable to primates suggests that neuroscientists now have a solid grasp of how object recognition works in the brain.

To see how current networks hold up, the MIT scientists started by testing primates. They implanted arrays of electrodes in the inferior temporal (IT) cortex and area V4 (a part of the visual system that feeds into the IT cortex) of the primates' brains. This allowed them to see how neurons related to object recognition responded when the animals looked at various objects in 1,960 images. The viewing time per image was a mere 100 milliseconds, which is long enough for humans to recognize an object.

They then compared these results with those of the latest deep neural networks. These networks produce arrays of numbers when fed an image – different numbers for different images. If it groups similar objects into similar clusters in this number matrix representation, it's deemed accurate. "Through each of these computational transformations, through each of these layers of networks, certain objects or images get closer together, while others get further apart," explains lead author Charles Cadieu.

The best network, developed by researchers at New York University, classified objects as well as the macaque - a medium-sized Old World monkey - brain. That's the good news. The bad is that they don't know why. Neural networks are learning from massive datasets containing millions or billions of images, churning through the information with help from the high-performance graphical processing units that power the latest video games. But nobody knows quite what is going on in there as the networks refine their own algorithms.

Using algorithms based on the swarming behavior of ants and bees, the U.S. Navy is turning to driverless boats to protect its ships.

This August, on the James River in Virginia, the U.S. Navy staged the kind of scene you’d expect to see at the beginning of a James Bond movie. As a large ship moved through the water, a helicopter overhead spotted an unidentified boat approaching and sent a warning to a small fleet of escort boats. Some were armed with loudspeakers, others with flashing lights, another with a .50 caliber machine gun.

Once the fleet zeroed in on the threatening vessel with radar and infrared sensors, some of the escort boats broke away and quickly encircled it. They flashed lights and blasted warnings through loudspeakers. Threat resolved.

All of the escort boats were unmanned—and yet they moved together as a group, thanks to what’s known as “swarm intelligence.”

Himalayas and tropical regions likely next hotspots for language extinction. The world's roughly 7000 known languages are disappearing faster than species, with a different tongue dying approximately every 2 weeks. Now, by borrowing methods used in ecology to track endangered species, researchers have identified the primary threat to linguistic diversity: economic development. Though such growth has been shown to wipe out language in the past on a case-by-case basis, this is the first study to demonstrate that it is a global phenomenon, researchers say.

Many people know about the threatened polar bear and extinct passenger pigeon, but few have heard of endangered and extinct languages such as Eyak in Alaska, whose last speaker died in 2008, or Ubykh in Turkey, whose last fluent speaker died in 1992, says Tatsuya Amano, a zoologist at the University of Cambridge in the United Kingdom and lead author of the new study. It’s well known that economic growth or the desire to achieve it can drive language loss, he notes—dominant languages such as Mandarin Chinese and English are often required for upward mobility in education and business, and economic assistance often encourages recipients to speak dominant languages. Whereas specific case studies demonstrate such forces at work, such as the transition from Cornish to English in the United Kingdom and from Horom to English in Nigeria, this is the first study to examine losses worldwide and rank economic growth alongside other possible influences, he says.

Data on the number and location of surviving fluent speakers of endangered languages are scant, but Amano and colleagues used the most complete source available—an online repository called Ethnologue—for their analysis, he says. From the database, the group was able to calculate the geographical range, number of speakers, and rate of speaker decline for languages worldwide and map that data within square grid cells roughly 190 km across, spanning the entire globe. Although they were able to obtain information about the range and number of speakers for more than 90% of the world’s estimated 6909 languages, they could only glean details about the rate of decline or growth for 9%, or 649, of those languages, Amano notes.

Next, they looked for correlations between language loss and factors such as a country's gross domestic product and levels of globalization as calculated by an internationally recognized index. In addition, they examined environmental factors such as altitude, which might contribute to language loss by affecting how easily communities can communicate and travel.

Of all the variables tested, economic growth was most strongly linked to language loss, Amano says. Two types of language loss hotspots emerged from the study, published online today in the Proceedings of the Royal Society B. One was in economically well developed regions such as northwestern North America and northern Australia; a second was in economically developing regions such as the tropics and the Himalayas. Certain aspects of geography seemed to act as a buffer or threat, Amano says. For example, recent declines appear to occur faster in temperate climates than in the tropics or mountainous regions—perhaps because it is easier to travel in and out of temperate regions, Amano says. More research is necessary to determine precisely what it is about economic development that kills languages, he adds. Figuring out how growth interacts with other factors such as landscape is the next step, he says.

"This is the first really solid statistical study I've seen which shows principles about language decline that we've know about, but hadn't been able to put together in a sound way," says Leanne Hinton, a linguist at the University of California, Berkeley. Economics is far from the whole story, however, she says. In the United States, for example, current attitudes toward endangered tongues stem in large part from historical policies that forced young American Indians to eschew their native tongues in order to learn English, she says. Generations of disease, murder, and genocide—both historic and present, in some regions—have also played an important role and were not included in the new study's analysis, she says.

Although the study is silent on the subject of interventions to help preserve endangered languages, there is a range of revitalization efforts that can serve as examples, such as the incorporation of the Hawaiian language into school curricula and daily government operations, she says.

Vax, a game by Ellsworth Campbell and Isaac Bromley, explores how a disease spreads through a network, starting with just one infected person. It's a simple concept that works well.

When you start the game, you have a network of uninfected people. The more connected a person is, the more chances that person can infect others upon his or her own infection. Your goal is to strategically administer a limited supply of vaccinations and to quarantine people to prevent as many infections as you can.

Memes were originally framed in relationship to genes. In The Selfish Gene, Dawkins claimed that humans are “survival machines” for our genes, the replicating molecules that emerged from the primordial soup and that, through mutation and natural selection, evolved to generate beings that were more effective as carriers and propagators of genes. Still, Dawkins explained, genes could not account for all of human behavior, particularly the evolution of cultures. So he identified a second replicator, a “unit of cultural transmission” that he believed was “leaping from brain to brain” through imitation. He named these units “memes,” an adaption of the Greek word mimene, “to imitate.”Dawkins’ memes include everything from ideas, songs, and religious ideals to pottery fads. Like genes, memes mutate and evolve, competing for a limited resource—namely, our attention. Memes are, in Dawkins’ view, viruses of the mind—infectious. The successful ones grow exponentially, like a super flu. While memes are sometimes malignant (hellfire and faith, for atheist Dawkins), sometimes benign (catchy songs), and sometimes terrible for our genes (abstinence), memes do not have conscious motives. But still, he claims, memes parasitize us and drive us.

In the seminal work 'An Evolutionary Approach to Norms', Axelrod identified internalization as one of the key mechanisms that supports the spreading and stabilization of norms. But how does this process work? This paper advocates a rich cognitive model of different types, degrees and factors of norm internalization. Rather than a none-or-all phenomenon, we claim that norm internalization is a dynamic process, whose deepest step occurs when norms are complied with thoughtlessly. In order to implement a theoretical model of internalization and check its effectiveness in sustaining social norms and promoting cooperation, a simulated web-service distributed market has been designed, where both services and agents' tasks are dynamically assigned. Internalizers are compared with agents whose behaviour is driven only by self-interested motivations. Simulation findings show that in dynamic unpredictable scenarios, internalizers prove more adaptive and achieve higher level of cooperation than agents whose decision-making is based only on utility calculation.

Self-Policing Through Norm Internalization: A Cognitive Solution to the Tragedy of the Digital Commons in Social Networks by Daniel Villatoro, Giulia Andrighetto, Rosaria Conte and Jordi Sabater-Mir http://jasss.soc.surrey.ac.uk/18/2/2.html

What will be the growth of the Gross Domestic Product (GDP) or the competitiveness of China, United States, and Vietnam in the next 3, 5 or 10 years? Despite this kind of questions has a large societal impact and an extreme value for economic policy making, providing a scientific basis for economic predictability is still a very challenging problem. Recent results of a new branch—Economic Complexity—have set the basis for a framework to approach such a challenge and to provide new perspectives to cast economic prediction into the conceptual scheme of forecasting the evolution of a dynamical system as in the case of weather dynamics. We argue that a recently introduced non-monetary metrics for country competitiveness (fitness) allows for quantifying the hidden growth potential of countries by the means of the comparison of this measure for intangible assets with monetary figures, such as GDP per capita . This comparison defines the fitness-income plane where we observe that country dynamics presents strongly heterogeneous patterns of evolution. The flow in some zones is found to be laminar while in others a chaotic behavior is instead observed. These two regimes correspond to very different predictability features for the evolution of countries: in the former regime, we find strong predictable pattern while the latter scenario exhibits a very low predictability. In such a framework, regressions, the usual tool used in economics, are no more the appropriate strategy to deal with such a heterogeneous scenario and new concepts, borrowed from dynamical systems theory, are mandatory. We therefore propose a data-driven method— the selective predictability scheme —in which we adopt a strategy similar to the methods of analogues , firstly introduced by Lorenz, to assess future evolution of countries.

The question What is Complexity? has occupied a great deal of time and paper over the last 20 or so years. There are a myriad different perspectives and definitions but still no consensus. In this paper I take a phenomenological approach, identifying several factors that discriminate well between systems that would be consensually agreed to be simple versus others that would be consensually agreed to be complex - biological systems and human languages. I argue that a crucial component is that of structural building block hierarchies that, in the case of complex systems, correspond also to a functional hierarchy. I argue that complexity is an emergent property of this structural/functional hierarchy, induced by a property - fitness in the case of biological systems and meaning in the case of languages - that links the elements of this hierarchy across multiple scales. Additionally, I argue that non-complex systems "are" while complex systems "do" so that the latter, in distinction to physical systems, must be described not only in a space of states but also in a space of update rules (strategies) which we do not know how to specify. Further, the existence of structural/functional building block hierarchies allows for the functional specialisation of structural modules as amply observed in nature. Finally, we argue that there is at least one measuring apparatus capable of measuring complexity as characterised in the paper - the human brain itself.

One of the more fascinating areas of science that has emerged in recent years is the study of networks and their application to everyday life. It turns out that many important properties of our world are governed by networks with very specific properties.

These networks are not random by any means. Instead, they are often connected in the now famous small world pattern in which any part of the network can be reached in a relatively small number of steps. These kinds of networks lie behind many natural phenomena such as earthquakes, epidemics and forest fires and are equally ubiquitous in social phenomena such as the spread of fashions, languages, and even wars.

So it should come as no surprise that the same kind of network should exist in the legal world. Today, Marios Koniaris and pals at the National Technical University of Athens in Greece show that the network of links between laws follows exactly the same pattern. They say their network approach provides a unique insight into the nature of the law, the way it has emerged and how changes may influence it in the future.

Though they started at opposite ends of the socioeconomic spectrum, McCulloch and Pitts were destined to live, work, and die together. Along the way, they would create the first mechanistic theory of the mind, the first computational approach to neuroscience, the logical design of modern computers, and the pillars of artificial intelligence. But this is more than a story about a fruitful research collaboration. It is also about the bonds of friendship, the fragility of the mind, and the limits of logic’s ability to redeem a messy and imperfect world.

Nice story. On the other hand, it exemplifies the magic of finding what you're looking for:

"Which got McCulloch thinking about neurons. He knew that each of the brain’s nerve cells only fires after a minimum threshold has been reached: Enough of its neighboring nerve cells must send signals across the neuron’s synapses before it will fire off its own electrical spike. It occurred to McCulloch that this set-up was binary—either the neuron fires or it doesn’t. A neuron’s signal, he realized, is a proposition, and neurons seemed to work like logic gates, taking in multiple inputs and producing a single output. By varying a neuron’s firing threshold, it could be made to perform “and,” “or,” and “not” functions."

Oh yes. After days of mulling over binary operations one begins to see strange things. Hallucinations, flashes of bits saturating everything. All of a sudden, one no longer inhabits a world of things, but a world of bits. The bits, deeply burned into the cornea like a cataract, never seem to get out the way. Is that a brain, or is it bit soup? Ahh, I never thought about it that way. What a *nice* way to think about it.

Interesting Section:

"There was a catch, though: This symbolic abstraction made the world transparent but the brain opaque. Once everything had been reduced to information governed by logic, the actual mechanics ceased to matter—the tradeoff for universal computation was ontology. Von Neumann was the first to see the problem. He expressed his concern to Wiener in a letter that anticipated the coming split between artificial intelligence on one side and neuroscience on the other. “After the great positive contribution of Turing-cum-Pitts-and-McCulloch is assimilated,” he wrote, “the situation is rather worse than better than before. Indeed these authors have demonstrated in absolute and hopeless generality that anything and everything … can be done by an appropriate mechanism, and specifically by a neural mechanism—and that even one, definite mechanism can be ‘universal.’ Inverting the argument: Nothing that we may know or learn about the functioning of the organism can give, without ‘microscopic,’ cytological work any clues regarding the further details of the neural mechanism."

The idea here is that the map from behavior to neural mechanism is one to many. They are many turing complete circuit topologies, so the idea of finding THE circuit for behavior or action X breaks down.

Nice multi-agent experiment showing the emergence of friendliness and the thinking on mode other's, after all a human advantage, neurocientist explain it by mirror neurons. Is the ultimate reason for the existence of Facebook and such.

People have wanted to understand our motivations, thoughts and behaviors since the ancient Greeks inscribed “know thyself” on the Temple of Apollo at Delphi. And understanding the brain’s place in health and disease is one of this century’s greatest challenges – take Alzheimer’s, dementia and depression for example.

There are many exciting contributions from neuroscience that have given insight into our thoughts and actions. Three neuroscientists have just been awarded the 2014 Nobel Prize for their discoveries of cells that act as a positioning system in the brain – in other words, the mechanism that allows us to navigate spaces using spatial information and memory at a cellular level.

There are many exciting contributions from neuroscience that have given insight into our thoughts and actions. For example, the neural basis of how we make fast and slow decisions and decision-making under conditions of uncertainty. There is also an understanding how the brain is affected by stress and how these stresses might switch our brains into habit mode, for example operating on “automatic pilot” and forgetting to carry out planned tasks, or the opposite goal-directed system, which would see you going out of your usual routine, for example, popping into a different supermarket to get special ingredients for a recipe.

Disruption in the balance between the two is evident in neuro-psychiatric disorders, such as obsessive compulsive disorder, and recent evidence suggests that lower grey matter volumes in the brain can bias towards habit formation. Neuroscience is also demonstrating commonalities in disorders of compulsivity, methamphetamine abuse and obese subjects with eating disorders.

Neuroscience can challenge previously accepted views. For example, major abnormalities in dopamine function were thought the main cause of adult attention deficit hyperactivity disorder (ADHD). However, recent work suggests that the main cause of the disorder may instead be associated with structural differences in grey matter in the brain.

What neuroscience has made evidently clear is that changes in the brain cause changes in your thinking and actions, but the relationship is two-way. Environmental stressors, including psychological and substance abuse, can also change the brain. We also now know our brains continue developing into late adolescence or early young adulthood, it is not surprising that these environmental influences are particularly potent in a number of disorders during childhood and adolescence including autism.

If you think you're in complete control of your destiny or even your own actions, you're wrong. Every choice you make, every behavior you exhibit, and even every desire you have finds its roots in the social universe. Nicholas Christakis explains why individual actions are inextricably linked to sociological pressures; whether you're absorbing altruism performed by someone you'll never meet or deciding to jump off the Golden Gate Bridge, collective phenomena affect every aspect of your life. By the end of the lecture Christakis has revealed a startling new way

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